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Hi Ivm thank you very much for the detailed answer..... it helps me a lot. Another thing, with the ML approach can I compare a model with only positive event as predictor to a model with positive event and negative event as predictors (with the X square distribution with 1 [in case of only added fix effect] or 2 [in case of random and fix effect] degree of freedom ? and lastly, is the difference between the random and the repeated statement that the random statement models the between subject random effect (e.g,. how positive event is associated differently with mood for each subject) and the repeated statement models the within person variation? Again, thanks a lot Eran     ... View more

I will try to be more specific Let's say that I have a diary data from several subjects. That is my data has a structure of  days nested within persons. Let's say that my hypothesis is that daily mood will be predicted by daily positive and negative events. So I want to use multi-level models analyses (MLM/HLM) with proc mixed to test my hypotheses.   My model would be something like this level 1: MOODjt= b 0j + b 1j *positive event + b 2j* negative event + e ij (j=person j, t=day t) And the level 2 (random) of the model would be- Level 2:  b 0j = γ 00 + u oj b 1j = γ 10 + u 1j b 2j = γ 20 + u 2j To test this model I would use the following proc mixed syntax       proc mixed covtest data=X; class id day; model mood= positive_event negative_event/s ; random intercept positive_event negative_event/subject = id; repeated day/subject = id type = ar(1); run; my question is if I want to constrain the positive_event and Negative_event to have the same weights (let's say of 1) in order to test if they are different from each other (that is, if the constrained model has less fit than the unconstrained model), how can I do that? Put it more simply, if I want that a certain fix effect will have specific value in order to test how it influences the fit of the model, how can I do it? Thanks a lot Eran ... View more